计算机科学
颗粒过滤器
威布尔分布
算法
标准差
惯性导航系统
地形
水下
跳跃式监视
高斯分布
卡尔曼滤波器
控制理论(社会学)
统计
数学
人工智能
地质学
物理
方向(向量空间)
生态学
海洋学
几何学
控制(管理)
量子力学
生物
作者
Tian Zhou,Tianhao Wang,Jiaqi Gao,Qijia Guo,Zhenyu Yan
标识
DOI:10.1088/1361-6501/ac7a08
摘要
Abstract Inertial navigation systems (INSs) are widely used for autonomous underwater vehicle navigation, with excellent short-term precision. However, the positioning error of an INS accumulates with time. It is imperative to adopt other approaches to mitigate drift errors, especially for long-term sailing tasks. To that end, underwater terrain-based navigation (TBN) is effective in bounding the drift errors. In particular, the particle filtering (PF) method is extensively employed in TBN to tackle the highly nonlinear measurement equation. In our previous work, the statistical properties of the digital terrain model gradient were exploited and we proposed the use of three probability density functions to characterize the normalized data, i.e. Gaussian, gamma and Weibull distributions. In this paper, the likelihood was modified according to the gradient fitting results, i.e. an optimal distribution selection. Moreover, to prevent the measurement data with large gradients from generating particles with lower weights, a pre-screen procedure was proposed to stabilize particle filter sampling. As shown and demonstrated in simulations, our proposed improved PF method outperforms the comparative ones in terms of root mean square error and standard deviation, as well as accuracy, especially for long-term sailing tasks. In terms of computational cost, a smaller number of measurement data are employed and the proposed method is faster than the standard PF method.
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